Book Image

The Deep Learning Workshop

By : Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
Book Image

The Deep Learning Workshop

By: Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)
Preface

The Embedding Layer

In Chapter 4, Deep Learning for Text – Embeddings, we discussed that we can't feed text directly into a neural network, and therefore need good representations. We discussed that embeddings (low-dimensional, dense vectors) are a great way of representing text. To pass the embeddings into the neural network's layers, we need to employ the embedding layer.

The functionality of the embedding layer is two-fold:

  • For any input term, perform a lookup and return its word embedding/vector
  • During training, learn these word embeddings

The part about looking up is straightforward – the word embeddings are stored as a matrix of the V × D dimensionality, where V is the vocabulary size (the number of unique terms considered) and D is the length/dimensionality of each vector. The following figure illustrates the embedding layer. The input term, "life", is passed to the embedding layer, which performs a lookup and returns...